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Python 3.6 for beginners
__TOC__ Python is an , , . Created by and first released in 1991, Python's design philosophy emphasizes with its notable use of . Its language constructs and approach aim to help programmers write clear, logical code for small and large-scale projects. Python is and . It supports multiple s, including , object-oriented, and . Python is often described as a "batteries included" language due to its comprehensive . Python uses whitespace indentation, rather than curly brackets or keywords, to delimit blocks. An increase in indentation comes after certain statements; a decrease in indentation signifies the end of the current block. Python's large standard library, commonly cited as one of its greatest strengths, provides tools suited to many tasks. It includes modules for creating graphical user interfaces, connecting to relational databases, generating pseudorandom numbers, arithmetic with arbitrary precision decimals, manipulating regular expressions, and unit testing. As of March 2018, the Python Package Index (PyPI), the official repository for third-party Python software, contains over 130,000 packages with a wide range of functionality When speed is important, a Python programmer can move time-critical functions to extension modules written in languages such as C Anaconda I strongly recommend installing . It comes with and preinstalled. Anaconda also comes with which makes it much easier to find and correct errors. Package versions are managed by the package management system . Conda was originally developed to solve difficult package management challenges faced by Python data scientists. The big difference between Conda and the pip package manager is in how package dependencies are managed, which is a significant challenge for Python data science and the reason Conda was created. pip installs all Python package dependencies required, whether or not those conflict with other packages previously installed. So a working installation of, for example, Google TensorFlow can suddenly stop working when you pip-install a new package that needs a different version of the NumPy library. More insidiously, everything might still appear to work, but now you get different results, or you are unable to reproduce the same results elsewhere because you didn't pip-install in the same order. Conda analyses your current environment, everything you have installed, any version limitations you specify (e.g. you only want tensorflow >= 2.0) and figures out how to install compatible dependencies. Or it will tell you that what you want can't be done. Comments # comments follow a pound sign Help Sounds To play a sound when your program is finished use: import winsound duration = 1000 # milliseconds freq = 440 # hz winsound.Beep(freq,duration) Time To time your program use: import time StartTime=time.time() # put the rest of your program here EndTime=time.time() print ('Time in seconds:', EndTime-StartTime) Exit To exit your program at any point just use: raise SystemExit() Math 3+5 =8 3-5 =-2 3*5 =15 3/5 = 0.6 5//3 = 1 # Floor -5//3 = -2 # (Rounding toward negative infinity) round(1.8) = 2 # (Rounding toward nearest integer) round(1.5) = 2 # (Rounding toward even) 3%5 = 3 # Mod 3**5 = 243 ~5 = -6 # -x-1 (Inverts bits) 1<<2 = 4 # (Shifts bits to the left) 4>>2 = 1 # (Shifts bits to the right) Rounding towards negative infinity means that the equation (a + b)//b a//b + 1 is always true and that the equation b*(a//b) + a%b a is valid for both positive and negative values of a. Back to top Variables : Back to top Dictionaries Ordered lists External link: https://rushter.com/blog/python-lists-and-tuples/ Back to top Input and output name = "john" age = 36 print(name, "is", age, "years old") # default separater is a single space. default end is new line print("this is printed on a new line") print("But we can change the def", "ault separator and default end", sep="", end="") print("This is not printed on a new line") print("a\tbx\tc\n12345678901234567890") # \t is tab and \n is new line. print(r'a\tbx\tc\n12345678901234567890') # r = raw text. Slashes are treated like ordinary text. your_input = input("please enter your input: ") john is 36 years old this is printed on a new line But we can change the default separator and default endThis is not printed on a new line a bx c 12345678901234567890 a\tbx\tc\n12345678901234567890 please enter your input: Back to top Loops : for num in range(1000, 2000, 100): # range is an iterator not a list or array ::print (num) # prints 1000 to 1900 in steps of 100 num = 0 while num < 100000000: # Thats 100,000,000. It runs in about 10 seconds ::num = num + 1 Back to top Conditional statements : young = age < 10 old = age > 65 middle_age = (45 < age < 65) if young: # dont forget the colon at the end ::print (name, "is young") elif old: ::print (name, "is old") elif middle_age: ::pass # Do nothing else: ::print (name, "is neither young nor old nor middle age") Back to top Functions : def convert_to_celcius(fahrenheit): ::"""Converts fahrenheit to celcius""" # documentation that is printed out when we use help("convert_to_celcius") ::return (fahrenheit-32)*5/9 print (convert_to_celcius(212)) Back to top Importing modules There is nothing fancy about a module. Its just a program containing function definitions. The program is executed when it is imported unless it was previously imported. You can easily create your own modules. After importing a module you can use help("module name") to get info. ::Template:Help("time") ::Template:Help("math") dir(module_name) # returns a list of all the functions in the module. Easier to read if converted to an array. import time start = time.time() time.sleep(15) # delay for 15 seconds end = time.time() print(end - start) # time in seconds import math x = math.sqrt(9) # x = sqrt(9) wont work. You must use math.sqrt import math as mt x = mt.sqrt(9) from math import sqrt x = sqrt(9) # no need for math.sqrt since sqrt has been imported into the current namespace import random rand1 = random.randint(0,999999) # random integer between 0 and 999,999 Back to top Classes Tutorial Calling methods is very similar to calling functions in a module. Instead of module.function(arguments) you use class.method(arguments). str.capitalize("string") # returns the string with first letter capitalized In a perfect world modules and classes would be the same thing because every module would correspond to exactly one class of object. But we dont live in that world. type(any_object) # returns the class of the object When we call a method, the object itself is passed as the first parameter (called "self"). class Person: :def __init__(self, name, birthday): ::self.name = name ::self.birthday = birthday :def age(self): ::today = datetime.date.today() ::age = today.year - self.birthdate.year ::return age person1 = Person("Bill", datetime.date(1981, 12, 27) ) Back to top Text files with open("TextFile.txt", "r") as file: ::contents = file.read() with open("TextFile.txt", "r") as file: ::List_of_lines = file.readlines() with open("TextFile.txt", "w") as output_file: ::output_file.write(contents) Back to top Database Creating: import sqlite3 con = sqlite3.connect('test.db') cur = con.cursor() cur.execute('CREATE TABLE test(name TEXT, age INTEGER)') cur.execute('INSERT INTO test VALUES("John", 33)') cur.execute('INSERT INTO test VALUES("Alice", 21)') x=18 cur.execute('INSERT INTO test VALUES(?, ?)', x) con.commit() # changes are not committed until this command cur.execute('SELECT * FROM test WHERE age > 5 ORDER BY name') whole_table = cur.fetchall() print (whole_table) con.close() Updating" import sqlite3 con = sqlite3.connect('test.db') cur = con.cursor() cur.execute('DELETE FROM test WHERE name = "John"') cur.execute('UPDATE test SET age = 99 WHERE name = "Alice"') # if there is more than one Mary then they both get updated cur.execute('SELECT * FROM test WHERE age > 5 ORDER BY name') whole_table = cur.fetchall() print (whole_table) con.close() Back to top Pandas Tutorial and Cheatsheet pandas = panels of data (multi-dimensional data) A pandas dataframe (spreadsheet) is normally created from a list or a list of lists. We could create a dataframe from an array but then all the data would have to be of the same type. Working with dataframes is faster than working with lists because dataframes are two dimensional numpy arrays. Each data type is stored in a separate block of memeory. All the integers are together in one block. All the floats are together in one block...etc. numpy dtypes that are also accepted in pandas: [[numpy.number, [[numpy.integer, [[numpy.signedinteger, [numpy.int8, numpy.int16, numpy.int32, numpy.int64, numpy.int64, numpy.timedelta64], [numpy.uint8, numpy.uint16, numpy.uint32, numpy.uint64, numpy.uint64]]], [[numpy.floating, [numpy.float16, numpy.float32, numpy.float64, numpy.float128], [numpy.complex64, numpy.complex128, numpy.complex256]]]]], [[numpy.character, [numpy.bytes_, numpy.str_], [numpy.record]]], numpy.bool_, numpy.datetime64, numpy.object_]] Pandas also adds two dtypes: categorical and datetime64tzhttps://stackoverflow.com/a/27232309/10366044 Back to top Internet The following code prints each line of the webpage minus most of the html markup. tag = False quote = False import urllib.request with urllib.request.urlopen("https://en.wikipedia.org/wiki/Hiroshi_Sakagami") as webpage: for line in webpage: out = "" line = line.strip() line = line.decode('utf-8') if line "": print (line) elif line0:7 "": line = "" else: for c in line: if c '<' and not quote: tag = True elif c '>' and not quote: tag = False elif (c '"' or c "'") and tag: quote = not quote elif not tag: out = out + c if out != "": print(out) x=input("press enter when done") Back to top Numpy Arrays numpy arrays are fast because they work with continuous blocks of memory. numpy.random.randint(low, high, size) # returns array of random integers that are less than "high" import numpy as np x = np.array([1,2,3,4,5,6]) print (x) [2 3 5 6] Math operations + and * operate elementwise and create a new matrix. To modify an existing array use += and *= For matrix multiplication use @ The identity matrix can be created easily with numpy.eye(n): print (np.eye(4)) [0. 0. 0. 1. 0. 0. 0. 1. 0. 0. 0. 1.] The inverse of matrix m is: np.linalg.inv(m) Back to top Scipy tutorial To import two subpackages you would use: from scipy import subpackage1, subpackage2 import scipy as sp from scipy import constants print (constants.physical_constantsparticle mass") # produces the following tuple: (6.64465723e-27, 'kg', 8.2e-35) Back to top Sympy Galgebra tutorial Back to top Matplotlib Tutorial The Matplotlib API Bizarrely, in matplotlib.pyplot states are preserved across function calls. Line or scatter plot If we had wanted a log log plot we would have used: ax=plt.gca() ax.set_xscale('log') ax.set_yscale('log') Bar graph Bar graph: import numpy as np import matplotlib.pyplot as plt fig=plt.gcf() fig.set_size_inches(14.4,1.3*9.6) data = [ 0, 5, 1, 16, 20, 2, 26, 26, 30, 32] positions = np.arange(10) width = 0.75 ax=plt.gca() ax.set_axisbelow(True) ax.set_xscale('linear') ax.set_yscale('linear') plt.grid(which='major', axis='both', color='black', linestyle='-') plt.minorticks_on() plt.grid(which='minor', axis='both', color='#CCCCCC', linestyle='--') plt.axis(10, 0, 35) p1 = plt.bar(positions, data, width) plt.ylabel('y-axis label') plt.title('Title') plt.savefig("File.name2.png", dpi=100) plt.show() See also: subplots Back to top Jupyter Tutorial Back to top Intel® Distribution for Python Math Kernel Library Numba https://developer.nvidia.com/how-to-cuda-python Back to top External links * https://snakify.org/en/ (for teachers) * https://www.packtpub.com/tech/Python Copyright References Category:Programming